compare-mt: A Tool for Holistic Comparison of Language Generation Systems (1903.07926v2)

Abstract: In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic $n$-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. The code is available at https://github.com/neulab/compare-mt

• The paper presents compare-mt, which provides a comprehensive method to evaluate language generation outputs using word-level, sentence-level, and n-gram analyses.
• It demonstrates key findings, including higher BLEU scores for neural MT systems and nuanced differences in low-frequency word handling.
• The tool’s advanced features, such as label-wise and source-side analyses, offer actionable diagnostics to refine machine translation performance.

Overview of "compare-mt: A Tool for Holistic Comparison of Language Generation Systems"

The paper presents "compare-mt," an analytical tool designed to compare and evaluate the outcomes of language generation systems, with a specific focus on tasks such as machine translation (MT). The tool aims to provide users with comprehensive insights into the differences between system outputs, facilitating deeper analysis and potential enhancements of LLMs.

Purpose and Functionality

"compare-mt" is positioned as a versatile, open-source Python package that offers a broad array of analytical capabilities. Its core purpose is to address the opacity of traditional evaluation metrics like BLEU, ROUGE, and METEOR, which often fail to express the nuanced differences in system outputs. By enabling a more detailed examination of results, compare-mt assists researchers in identifying the specific elements contributing to performance discrepancies.

The tool offers a range of features:

• Word-Level Analysis: It evaluates word accuracy categorized by frequency, type, and other characteristics, using metrics such as precision and recall.
• Sentence-Level Analysis: This involves calculating metrics such as BLEU bucketed by sentence length or other features, offering a detailed breakdown of system performance on various types of sentences.
• N-Gram Analysis: The comparison of n-grams between systems reveals patterns of improvement or degradation, facilitating targeted adjustments in model training.
• Sentence Example Analysis: By isolating sentence cases with divergent outputs, compare-mt aids in pinpointing patterns of errors or successful translations.

Numerical Evidence and Insights

The paper provides empirical examples using Slovak-English machine translation systems, showcasing numerical evidence of the insights achievable with compare-mt. Notable findings include:

• Higher BLEU scores achieved by neural MT systems compared to phrase-based systems in certain scenarios, though with shorter sentence outputs.
• Differences in the handling of low-frequency words, where phrase-based systems exhibited better robustness.

These results illustrate the tool's efficacy in uncovering specific strengths and weaknesses of different system architectures, offering a foundation for their improvement.

Advanced Features

Beyond basic analyses, compare-mt introduces advanced analytical capabilities:

• Label-wise Abstraction: It allows analysis over labels such as POS tags, which can discern model performance concerning different syntactic categories.
• Source-Side Analysis: The tool can analyze accuracy with respect to source-language words, offering insights into translation fidelity and alignment accuracy.
• Likelihood Analysis: Through log likelihood assessments, researchers can examine probabilistic outputs on a word-by-word basis.

Implications and Future Directions

The implications of using compare-mt extend to both practical and theoretical realms. Practically, it can guide focused improvements in LLMs by diagnosing specific issues in performance. Theoretically, by facilitating a more granular understanding of system behavior, it could inform the development of new metrics or training paradigms tailored to address discovered weaknesses.

Given its extensible design, future developments could integrate more complex diagnostic features, potentially enhancing its utility for a broader range of language generation applications. For instance, its open architecture allows for the addition of custom metrics and analysis types, positioning compare-mt as a foundational tool in the evolving landscape of NLP research.

In conclusion, "compare-mt" represents a significant contribution to the toolkit available for researchers in language technologies, providing essential capabilities for the nuanced evaluation and comparison of language generation systems. Its ability to offer detailed, actionable insights underscores its value in driving advancements in LLM development and deployment.